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from cbench.commands import *
from cbench import state
from plumbum.machines.paramiko_machine import ParamikoMachine
from time import sleep
import _thread


    

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state.RUN_NAME = "Baseline_READ_MODIFY_WRITE_ALL_5_vs_m4.2xlarge_190"
state.WORKLOAD = "workloads/workload_read_modify_write"
#state.WORKLOAD = "workloads/workload_read"
#state.WORKLOAD = "workloads/workload_scan"


    

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print("Instances:")
list_instances()
print("Loading state from AWS..")
load_state()
print("Cluster instances: {0}".format(state.CLUSTER_INSTANCES))


    

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create_instances(5, state.CLUSTER_INSTANCES, type="i2.xlarge")
create_instances(1, state.YCSB_INSTANCES, 'ycsb', type="m4.2xlarge")


    

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print(state.CLUSTER_INSTANCES)


    

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#Wait for Instances to finish boot
sleep(5 * 60)


    

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# create the cluster
create_cluster(state.CLUSTER_INSTANCES)


    

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prepare_benchmark(workload=state.WORKLOAD, name=state.RUN_NAME)


    

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temp = state.CLUSTER_INSTANCES[-1:]

state.CLUSTER_INSTANCES = state.CLUSTER_INSTANCES[:-1]


    

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print(temp)


    

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state.CLUSTER_INSTANCES.append(temp[0])


    

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print(state.CLUSTER_INSTANCES)


    

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# Baseline benchmark
bench = _thread.start_new_thread(start_benchmark, (190, ["-p","maxexecutiontime=300"]))
sleep(5 * 60)
wait_for_finish()
gather_results()


    

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plot(state.RUN_NAME, granularity=10)


    

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# Max Load benchmark
for num in range(7):
    num = 170 + num * 10
    state.RUN_NAME = "MAX_READ_AGAIN_6_vs_m4.2xlarge_" + str(num)
    bench = _thread.start_new_thread(start_benchmark, (num, ["-p","maxexecutiontime=300"]))
    sleep(5 * 60)
    wait_for_finish()
    gather_results()
    # Wait for compaction etc. to finish
    sleep(1 * 60)


    

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print(state.CLUSTER_INSTANCES)
# Create additional instance
create_instances(1, state.CLUSTER_INSTANCES, type="i2.xlarge")
print("New instance: {0}".format(state.CLUSTER_INSTANCES[-1:]))


    

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scale_cluster(state.CLUSTER_INSTANCES[-1:])


    

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sleep(5)
state.CLUSTER_INSTANCES.append(temp[0])


    

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# Scaling benchmark
state.RUN_NAME = "Scale_READ_MODIFY_WRITE_ALL_5_vs_m4.2xlarge_190"
bench = _thread.start_new_thread(start_benchmark, (190, []))
sleep(3 * 60)
scale_cluster(state.CLUSTER_INSTANCES[-1:])
wait_for_finish()
gather_results()


    

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# Giving the cluster a short pause
sleep(2 * 60)


    

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#Reduce benchmark
state.RUN_NAME = "Reduce_READ_MODIFY_WRITE_ALL_5_vs_m4.2xlarge_190"
bench = _thread.start_new_thread(start_benchmark, (190, []))
sleep(3 * 60)
remove_cassandra_instance(state.CLUSTER_INSTANCES[-1])
wait_for_finish()
gather_results()


    

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#state.RUN_NAME = "Reduce_READ_MODIFY_WRITE_4_vs_m4.2xlarge_100_2"
wait_for_finish()
#gather_results()


    

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gather_results()


    

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cleanup_logs()


    

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terminate_all()


    

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plot("Scale_READ_MODIFY_WRITE_ALL_5_vs_m4.2xlarge_190", granularity=30)
plot("Reduce_READ_MODIFY_WRITE_ALL_5_vs_m4.2xlarge_190", granularity=30)


    

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#plot("Scale_READ_5_to_6_vs_m4.2xlarge_190", measurements=["avg"], op_types=["READ"], granularity=10)
plot("Reduce_READ_MODIFY_WRITE_5_vs_m4.2xlarge_190", measurements=["avg"], op_types=["READ"], granularity=10)


    

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gather_lois("Scale_READ_5_to_6_vs_m4.2xlarge_190")


    

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from cbench import util

for inst in state.CLUSTER_INSTANCES:
    util.docker_status(inst)


    

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print("Hello World!")